Method for distinguishing follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC)

ABSTRACT

Follicular thyroid adenoma (FTA) is distinguished from follicular thyroid carcinoma (FTC) by comparing amount of an expression product of at least one gene selected from the group consisting of DDIT3, ARG2, ITM1, C1orf24, TARSH, and ACO1 in a test follicular thyroid specimen to a normal control thyroid specimen. The test follicular thyroid specimen is identified as FTA if the amount of expression product of TARSH is equal to or greater in the test follicular thyroid specimen than in the normal control thyroid specimen. The test follicular thyroid specimen is identified as FTC if the amount of expression product of DDIT3, ARG2, ITM1, C1orf24, or ACO1 is greater in the test follicular thyroid specimen than in the normal control thyroid specimen.

The U.S. Government retains certain rights to this invention due to funding by the National Institutes of Health contract number NIH 98X-S146A.

FIELD OF THE INVENTION

The invention relates to the field of distinguishing thyroid diseases, and more particularly to the field of distinguishing follicular thyroid adenoma from follicular thyroid carcinoma.

BACKGROUND OF THE INVENTION

The incidence of thyroid cancer is increasing, with a global estimate of one-half million new cases this year. Thyroid carcinoma is usually first suspected by a physician when a solitary nodule is palpated on physical exam. Thyroid nodules, however, can be the result of a wide spectrum of causes, and a major concern is to accurately differentiate between benign and malignant nodules.

Cytology of a fine-needle aspiration (FNA) biopsy is the most widely used and cost-effective pre-operative test for initial thyroid nodule diagnosis (1). When FNA findings are diagnostic of papillary thyroid carcinoma, the specificity for malignancy approaches 95% (2). A common problem in clinical practice, however, is evaluation and management of thyroid tumors with a follicular pattern. FNA cytology cannot differentiate between follicular thyroid adenoma (FTA) and follicular thyroid carcinoma (FTC). Since cytology cannot distinguish between FTA and FTC they are often grouped together as indeterminate or follicular-patterned thyroid lesions. Surgical biopsy is needed to confirm FTA or FTC. Invasion through the tumor capsule or the blood vessels is an indicator of FTC. To provide an accurate diagnosis, most guidelines recommend surgical removal of a nodule diagnosed as having a follicular pattern. Complete thyroid resection and subsequent radioiodine therapy is indicated for those patients who ultimately have findings indicating carcinoma. Overall, only 8%-17% of these cytologically suspicious nodules are indeed malignant on histological examination (3).

Several genes have been reported to be associated with thyroid tumors. LGALS3 expression was proposed as a potential marker for pre-operative diagnosis of thyroid carcinoma (4-6). Subsequent findings, however, showed LGALS3 expression in benign lesions such as multinodular goiter and FTA (7,8). Recently, a chromosomal translocation t(2;3)(q13;p25) was reported in five of eight cases with FTC, but not in twenty cases with FTA (9). The authors suggested that the resulting PAX8/PPARG fusion gene could be useful in the diagnosis and treatment of thyroid cancer (9). This rearrangement, however, was found in 13%-30% of follicular adenomas (10-12). In addition, several molecular markers have been analyzed for their ability to discriminate between benign and malignant follicular tumors. The molecular markers include TPO, TP53, telomerase, and HMBE-1. Nonetheless, these candidate markers have not proved to have practical value for FNA pre-operative diagnosis of FTC (13-15). More recently cDNA array technology has been used to identify potentially important thyroid cancer-associated genes (16). Although many of the gene or gene patterns expressed in thyroid tumors have been described, the clinical problem of distinguishing FTC from FTA remains.

A large percentage of patients would, therefore, benefit greatly from improved diagnosis of FNA material. Improved diagnosis could reduce the number of surgeries, long-term health costs and post surgical complications. In particular, in many areas of the world where health care systems are over-burdened, limited resources for surgery could be directed more rapidly towards those with the highest risk of having carcinoma. Accurate molecular markers based on differential gene expression between FTA and FTC would be one means of improving the accuracy of diagnoses made from FNA.

BRIEF SUMMARY OF THE INVENTION

In one embodiment of the invention a method for distinguishing follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC) is provided. Amount of an expression product of at least one gene selected from the group consisting of DDIT3, ARG2, ITM1, C1orf24, TARSH, and ACO1 in a test follicular thyroid specimen is compared to the amount in a normal control thyroid specimen. The expression product is selected from the group consisting of protein and RNA. The test follicular thyroid specimen is identified as FTA if the amount of expression product of TARSH is equal to or greater in the test follicular thyroid specimen than in the normal control thyroid specimen or the test follicular thyroid specimen is identified as FTC if the amount of expression product of DDIT3, ARG2, ITM1, C1orf24, or ACO1 is greater in the test follicular thyroid specimen than in the normal control thyroid specimen.

In another embodiment of the invention a method for distinguishing follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC) is provided. Amount of an expression product of DDIT3, ARG2, and ITM1 in a test follicular thyroid specimen is compared to the amount in a normal control thyroid specimen. The expression product is selected from the group consisting of protein and RNA. The test follicular thyroid specimen is identified as FTC if the amount of expression product of DDIT3, ARG2, or ITM1 is increased in the test follicular thyroid specimen relative to the normal control thyroid specimen. The invention thus provides the art with methods for distinguishing follicular thyroid adenoma from follicular thyroid carcinoma.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows relative expression level determined by quantitative RT-PCR in twenty three samples of FTA and FTC (black bars) and in normal thyroid tissues (gray bars). Transcript levels were normalized to the average of ribosomal protein 8 and t-complex 1, which were uniformly expressed in all three thyroid SAGE libraries. Numbers 1-10 correspond to FTA and 11-23 to FTC as described in Table 2. The statistical analysis of RT-PCR values revealed that expression of genes DDIT3, ARG2, and ITM1 were significantly different at the 0.05 level, and C1 orf24 was significant at the 0.10 level. Genes ACO1 and TARSH may be involved in pathogenesis of thyroid tumor as well.

FIG. 2 shows quantitative RT-PCR products of three statistically significant genes (ITM1, ARG2, and DDIT3). The samples shown are FTAs (A), FTCs (C), normal thyroid tissues (N), thyroid carcinoma cell lines (CL) and negative control (NC). Genes DDIT3, ARG2, and ITM1 are expressed in most of the FTCs, the thyroid follicular carcinoma cell line (CL1), the papillary thyroid carcinoma cell line (CL2), and the undifferentiated thyroid carcinoma cell line (CL3), but not in normal and most FTAs. Case 6 (A6) expressed ARG2 and ITM1 and was misclassified according to our class-predicted genes. Universal human RNA (HUR) was used as a control. Ribosomal protein 8 is shown as a calibrator gene. The 100-bp DNA ladder (M) is shown in the far left and far right lanes. The results are shown in triplicate, and the numbers correspond to cases analyzed (Table 2). The product sizes are summarized in Table 3.

FIGS. 3A to 3L show immunohistochemical analysis of DDIT3 (FIGS. 3A-3F) and ARG2 (FIGS. 3G-3L) in paraffin embedded sections of FTA tissue and FTC tissue. FTC tissue exhibited strong brown immunostaining for DDIT3 (FIGS. 3D, 3E and 3F) and ARG2 (FIGS. 3J, 3K and 3L). In contrast, FTA tissue (FIGS. 3A, 3B, 3C, 3G, 3H and 3I) exhibited no immunoreactivity. The arrow in FIGS. 3D and 3F shows the vascular invasion in the FTC tissue and the follicular cells that are positive for DDIT3. The arrow in FIG. 3L shows normal thyroid tissue that was negative for ARG2 adjacent to a tumor area that was positive for ARG2. Hematoxylin was used as a nuclear counter stain. Original magnification is ×100 for FIGS. 3A-3E and 3G-3L, ×40 for FIG. 3F.

DETAILED DESCRIPTION OF THE INVENTION

It is a discovery of the present inventors that follicular thyroid adenoma (FTA) can be distinguished from follicular thyroid carcinoma (FTC) without removing the thyroid or obtaining a surgical sample of the thyroid. In particular, the inventors have discovered that FTA can be distinguished from FTC by comparing the amount of expression product of one or more of DDIT3¹, ARG2², ITM1³, C1orf24⁴, ACO1⁵, and TARSH⁶ in a test follicular thyroid specimen to the amount of expression product in a normal control thyroid specimen. ¹DNA-damage-inducible transcript 3D (SEQ ID NOS:1 and 2)²arginase type 2D (SEQ ID NOS:3 and 4)³integral membrane protein 1D (SEQ ID NOS:5 and 6)⁴chromosome 1 open reading frame 24 (SEQ ID NOS:7 and 8)⁵soluble aconitase 1 (SEQ ID NOS:9 and 10)⁶NESH binding protein (SEQ ID NOS:11 and 12)

TARSH expression is indicative of FTA, while expression of DDIT3, ARG2, ITM1, C1orf24, and ACO1 is indicative of FTC. Thus, if the amount of TARSH expression product is equal to or greater in the test follicular thyroid specimen than in the normal control thyroid specimen then the test follicular thyroid specimen can be identified as FTA. If the amount of any one, or more of DDIT3, ARG2, ITM1, C1orf24, and ACO1 expression product is greater in the test follicular thyroid specimen than in the normal control thyroid specimen then the test follicular thyroid specimen can be identified as FTC.

The amount of RNA expression in a test follicular thyroid specimen or a normal control thyroid specimen can be determined by methods well known in the art for measuring RNA expression. Examples of such methods include, but are not limited to, reverse transcriptase-polymerase chain reaction (RT-PCR), microarray analysis, northern blot analysis, differential hybridization, and ribonuclease protection assay. Such methods are well known in the art and are described in Sambrook et al., MOLECULAR CLONING: A LABORATORY MANUAL, Second Edition, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y., 1989 and Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, John Wiley & Sons, New York, N.Y., 1989.

The amount of protein expression in a test follicular thyroid specimen or a normal control thyroid specimen can be determined by methods well known in the art for measuring protein expression. Such methods include, but are not limited to, immunohistochemical staining, ELISA, immunoprecipitation, western blot (immunoblot), radioimmuno assay (RIA), and fluorescence-activated cell sorting (FACS). Such methods are described in Sambrook (1989) and Ausubel (1989).

Follicular thyroid specimens are obtained from a thyroid of a human and determined histologically to have a follicular pattern, if a precise diagnosis is not achievable. The specimen can be obtained by any method known in the art for obtaining a thyroid specimen. Fine-needle aspiration (FNA) biopsy sampling is an exemplary method. The test follicular thyroid specimen can be obtained prior to removal of the thyroid. However, the specimen can also be surgically removed thyroid tissue. A normal control thyroid specimen can be, for example, an FNA biopsy from a patient with a normal thyroid. Alternatively universal reference total human RNA or normal thyroid total RNA can be used as the normal human control thyroid specimen. Similarly universal reference total human protein or normal human thyroid total protein can be used as a control.

Differential expression between test and control samples are used to make a diagnostic determination. The amount of difference observed will depend on the particular gene, or the type of expression product, or the assay method, and on sample preparation. Generally, however, a difference which is reproducible or statistically significant can be used. Differences of at least 2-fold, 5-fold, 10-fold, 20-fold, 25-fold, have been observed and can be used to make a diagnosis. Genes which are not observably expressed in one of the two forms of follicular thyroid specimen may be compared without precise quantitation, for example visually.

DDIT3, also named GADD153 (growth arrest and DNA-damage inducible 153 gene), encodes a transcription factor that is induced in response to a large spectrum of genotoxic agents such as UV light, hypoxia, nutrient deprivation, environmental toxicants, and certain DNA-damaging agents (32,33). When induced, DDIT3 inhibits cell proliferation and promotes repair and/or apoptosis. The induction of DDIT3 leads to distinct biologic effects, such as growth stimulation, differentiation, invasiveness, and migration (34).

ITM1 encodes a highly conserved protein that contains ten to fourteen membrane-spanning domains. The protein does not have any identifiable domains with enzymatic activity and is probably not involved in direct transmembrane signaling. In addition, the transmembrane domain of ITM1 does not present any features of a transporter protein, such as an ATP binding cassette. However, Hong et al. hypothesize that ITM1 is a novel type of permease/transporter membrane protein (42). In humans, the ITM1 gene was mapped to human chromosome 11q23.3 (43,44). Interestingly, loss of heterozygosity was found in follicular adenomas at 11q (45,46).

ARG2 encodes an enzyme that catalyzes the hydrolysis of arginine to ornithine plus urea. At least two isoforms of mammalian arginase exists (ARG1 and ARG2). The two isoforms differ in their tissue distribution, subcellular localization, immunologic crossreactivity, and physiologic function (38). The type II isoform is located in the mitochondria and is expressed in extra-hepatic tissues, especially in the kidney (39). The physiologic role of this isoform is poorly understood, but it may play a role in nitric oxide and polyamine metabolism (40). Since polyamines are vital for cell proliferation, it is possible that the increased level of ornithine, due to the elevated arginase activity, may be linked to carcinogenesis development (41).

C1orf24 was described as a candidate marker for renal tumor, especially in early-stage renal carcinogenesis. The pattern of gene expression showed that C1orf24 is expressed in normal muscle, pancreas, colon, and prostate. The gene is very conserved in humans and rats, but the protein function is still unknown. A similarity with the DNAJ-1 motif, part of a chaperone system, has been described (47).

TARSH encodes a protein containing an Src-homology 3 (SH3) biding motif, a nuclear target sequence with no catalytic domain. Its biochemical and physiologic role has not been identified. TARSH is thought to be a binding partner of NESH-SH3, a member of the E3B1/ArgBP/Avi2/NESH family (48). Members of this family are involved in membrane ruffling and lamellipodia formation, which suggests that the loss of their expression could be involved in the mechanism of cell motility and metastasis. Re-expression of NESH suppresses motility and metastasis dissemination in the U-87 MG malignant glioma cell line (49). Although the binding activity between NESH and TARSH is yet to be confirmed, the loss of TARSH expression in FTCs might be a mechanism by which the follicular cells acquire motility and promote invasion. Another fact that supports this hypothesis is that the TARSH gene was mapped at 3q12, where loss of heterozygosity was found in FTC but not in FTA (50,51). Loss of heterozygosity in 3q was also correlated with survival in FTC (52).

Aconitase 1 (ACO1), also known as iron regulatory element binding protein 1 (IREB1), is a cytosolic protein which binds to iron-responsive elements (IREs). IREs are stem-loop structures found in the 5′ untranslated region (UTR) of ferritin mRNA, and in the 3′ UTR of transferrin receptor mRNA. The iron-induced binding of ACO1 to the IRE results in a repression of ferritin mRNA translation. Transferrin receptor mRNA is rapidly degraded, however, iron-induced binding of ACO1 to the IRE results in an inhibition of this rapid degradation. Thus, ACO1 plays a central role in cellular iron homeostasis.

All patents patent applications and references cited in this application are incorporated herein by reference in their entirety.

The following examples are offered by way of illustration and do not limit the invention disclosed herein.

EXAMPLES Example 1 Identification of Diagnostic Markers by Sage Analysis to Distinguish FTA from FTC

To directly address the problem of finding diagnostic markers that would distinguish FTA from FTC, gene expression was quantified in FTA tissues, FTC tissues, and normal thyroid tissues using serial analysis of gene expression (SAGE) (17). SAGE counts cDNA transcript tags in large numbers. Thus, SAGE analysis makes it possible to identify a restricted set of genes that are highly expressed in one tissue and not detectable in another. Transcript counts from FTC, FTA, and normal thyroid libraries were generated and compared.

SAGE Libraries.

One follicular thyroid adenoma, one follicular thyroid carcinoma, and one normal thyroid were chosen for SAGE (17). SAGE libraries were constructed using a microSAGE procedure (19) and were sequenced through the SAGE portion of the Cancer Genome Anatomic Project (20). Tags were extracted from automated sequence text files; and duplicate ditags, linker sequences, and repetitive tags were removed using SAGE 2000 software version 4.12. The Monte-Carlo simulation function (26) of the SAGE 2000 program was used to determine P values of differentially expressed genes. The full set of tag counts for all three libraries are available for downloading or analysis at the Cancer Genome Anatomy Project SAGE Genie Web site (21).

SAGE Analysis.

SAGE analysis of gene expression in FTA tissues, FTC tissues, and normal thyroid tissues resulted in a total of 359,478 tags. This represented 116,037 unique transcript tags. A SAGE tag sequence error rate of 6.8% (26) was used and we estimated that a total of 108,146 unique transcripts were detected. Of those, 10,048 were detected at least five times and 32,748 were detected at least two times.

Two comparisons were performed using SAGE 2000 software version 4.12: one between normal thyroid and FTA and one between FTA and FTC. Using SAGE software to perform Monte Carlo simulations (26), the expression level of 305 genes were found to be statistically significant if P value ≦0.0001. Of those 305 genes, seventy-three genes were found to be expressed only in FTC or only in FTA and normal thyroid tissue. Thirty-seven of these 305 transcripts were highly expressed in FTC tissues and not expressed in FTA or normal tissues. Thirty-six of the 305 genes were highly expressed in FTA and normal tissues and not expressed in FTC tissues.

Among the seventy-three candidates genes, those with the greatest fold-induction or fold-repression in FTC were first considered. Accordingly, seventeen transcripts were selected for RT-PCR validation. Twelve transcripts were highly expressed in the FTC library and five were expressed only in FTA and normal thyroid libraries. The expression levels of these genes in FTA and FTC libraries ranged from 43- to 10-fold. Table 1 lists the seventeen genes. For comparison, the transcript levels for well-characterized genes for normal thyroid physiology are also presented.

TABLE 1 Validated FTA and FTC differentially expressed genes and thyroid specific genes GenBank Accession (SEQ ID NOS: nucleotide/ Gene Tag sequence Normal⁷ Adenoma⁷ Carcinoma⁷ Transcript description⁸ amino acid) Location ontology⁹ Transcripts up regulated in FTC AACAATTGGG 0 0 19 DDIT3 - DNA-damage- NM_004083.2 12q13.1 Regulation (SEQ ID NO: 79) inducible transcript (SEQ ID cell cycle 3D¹⁰ NOS: 1/2) TTTCACAACA 2 0 21 ARG2 - arginase, type II NM_001172.2 14q24 Urea cycle (SEQ ID NO: 80) D¹⁰ (SEQ ID NOS: 3/4) TATTTACTCT 1 0 15 C1orf24, Chromosome 1 NM_052966 1q25 ND (SEQ ID NO: 81) open reading frame 24 (SEQ ID NOS: 5/6) TTGTAAATTA 0 0 19 PCSK2 -proprotein NM_002594 20p11.2 Cell-cell (SEQ ID NO: 82) convertase subtisilin/ (SEQ ID signaling kexin type 2 NOS: 13/14) CTGTAAATAT 0 0 12 ODZ1 (odd Oz/tenascin-M NM_014253 Xq25 Proteolysis (SEQ ID NO: 83) Drosophila melanogaster) (SEQ ID and homolog 1 NOS: 15/16) Peptidolysis GATAGGTCGG 0 0 27 ACO1, aconitase 1, NM_002197 9p22 Negative (SEQ ID NO: 84) soluble (SEQ ID regulation of NOS: 7/8) translation AGCTGAGCTA 3 0 11 DNASE2, deoxyribonu- NM_001375 19p13 DNA (SEQ ID NO: 85) clease II, lysosomal (SEQ ID metabolism NOS: 17/18) TAATGTATTC 1 0 23 Hypothetical protein NM_022484 7q31.32 ND (SEQ ID NO: 86) FLJ13576 (SEQ ID NOS: 19/20) GCTTTACTTT 5 0 27 ITM1- Integral membrane NM_152713 11q23 Protein amino (SEQ ID NO: 87) protein ID¹⁰ (SEQ ID acid NOS: 9/10) glycosylation TAAATACTTG 1 0 43 PDK4 -Pyruvate dehydro- NM_002612 7q21.3 Signal (SEQ ID NO: 88) genase kinase 4 (SEQ ID transduction NOS: 21/22) GCGCATCAAA 0 0 15 LOC92196, EST weakly XM_043500 2q24 Apoptosis (SEQ ID NO: 49) similar to death- (SEQ ID associated protein 1 NOS: 23/24) AGCAGGGCTC 4 0 17 PPP1R14B - protein XM_370630 11q13 Cell-cell (SEQ ID NO: 90) phosphatase 1, regula- (SEQ ID signaling tory subunit 14B NOS: 25/26) Transcripts up regulated in FTA CAGATAAGTT 3 12 0 COL14A1, collagen, type XM_044622 8q23 Cell-cell (SEQ ID NO: 91) XIV, α1 (undulin) (SEQ ID adhesion NOS: 27/28) CTTCAATCTT 7 37 0 TARSH - target of NESH- NM_015429 3q12 ND (SEQ ID NO: 92) SH3 protein (SEQ ID NOS: 11/12) GAGAGGAAGG 3 34 0 Putative Emu 1 NM_133455.1 22q12.2 ND (SEQ ID NO: 93) (SEQ ID NOS: 29/30) TGATCAATAT 3 10 0 NID2 NM_007361.1 14q21 Cell adhesion (SEQ ID NO: 94) (SEQ ID NOS: 31/32) GGTATGCTGT 2 10 0 EDNRB, Endothelial NM_000115.1 13q22 G-protein (SEQ ID NO: 95) receptor type B (SEQ ID coupled NOS: 33/34) receptor pathway Genes involved in thyroid function GATGAATAAA 75 38 0 TPO, Thyroid M17755 2p25 Thyroid (SEQ ID NO: 96) peroxidase (SEQ ID hormone NOS: 35/36) generation CGGTGAAGCA 134 67 130 TG, Thyroglobulin NM_003235 8q24.2 Thyroid (SEQ ID NO: 97) (SEQ ID hormone NOS: 37/38) generation ATGCTAAGAG 30 13 63 DIO2, Deiodinase, NM_000793 14q24.2 Thyroid (SEQ ID NO: 98) iodothyronine, type II (SEQ ID hormone NOS: 39/40) generation ⁷SAGE tags counts shown are after normalization to 100,000. ⁸Tag sequences were mapped to transcript sequence, confirmed by PCR and used to determine gene name and accession number. ⁹Gene classification was by biological process, ND- gene classification not defined. ¹⁰Genes differentially expressed in this study.

Four genes, DDIT3, ARG2, ITM1, and C1orf24, were found to have significant differential expression on an independent set of tumors. Interestingly, DDIT3, ARG2, and ACO1 can be modulated by hypoxia (33,55). Thus, we looked for CA9 expression in thyroid cancer since it is a hypoxia marker in other tumors (56). A higher expression of CA9 was found in 2 cases of FTCs which had a higher level of DDIT3 and ARG expression, but was not found in FTAs and normal tissues. Further testing is needed to determine whether hypoxia detected by CA9 is a marker for survival in thyroid carcinomas as it is in other tumors (57).

In addition to the markers of the present invention, SAGE also allowed identification of new genes, which mapped to a chromosome region that has already been described as important in thyroid carcinogenesis. A new hypothetical protein, FLJ13576, mapped to 7q31-32, and was over-expressed in 70% of FTC and the 2 FTA cases (cases 6 and 8, Table 2). This hypothetical protein contains a fibronectin type III domain, one of three types of internal repeats within the plasma protein fibronectin. The 7q31-32 locus contains other genes involved in thyroid carcinogenesis, such as the MET oncogene (53,54). Other genes mapped in this region were found such as SLC26A4 (solute carrier family 26, member 4) and NRCAM (neuronal cell adhesion molecule) and were found overexpressed in the FTC SAGE library. These results, in agreement with those obtained from comparative genomic hybridization analysis, where the observed gain of 7q31 and 7q21.1-q21.2 was the most frequent chromosomal imbalance in FTC, suggest that this locus duplication could be involved in thyroid carcinogenesis (51).

Since follicular cell interaction and differentiation is guided by a variety of factors, such as extracellular matrix glycoprotein and receptor and cell adhesion molecules, we also expected to find genes involved in this process to be differentially expressed between FTC and FTA. In fact, ODZ1 (tenascin M), ANXA1 (annexin 1), LAMB1 (laminin beta 1), MYL6 (myosin, light polypeptide 6), MSN (moesin), CLU (clusterin), TMSB4X (thymosin, beta 4), SPARC (osteonectin), CLDN1 (claudin 1), NID2 (nidogen 2), Emu 1, CANX (calnexin), SDC2 (syndecan 2), FMOD (fibromodulin), CDH1 (cadherin 1) and COL14A1 (undulin) were found differentially expressed in thyroid SAGE libraries. Some of these genes were described previously as being involved in thyroid tumor genesis, but they were not used to discriminate between FTA and FTC (30,58).

Example 2 RT-PCR Analysis of Genes Identified by Sage Analysis to Confirm Expression Level

To validate the differential gene expression profile predicted by SAGE, seventeen genes with the highest fold-induction were tested and analyzed for gene expression by quantitative real-time RT-PCR.

Tissue Samples.

For RT-PCR analysis, twenty-three primary tumors were obtained from patients initially diagnosed with follicular thyroid tumor. The tumors were frozen immediately after surgical biopsy. All samples were obtained from patients followed at Hospital São Paulo, Universidade Federal de São Paulo, and Hospital Helópolis, São Paulo, Brazil. The study was approved by the Ethics and Research Committees of the Universidade Federal de São Paulo and Hospital Heliópolis and was in agreement with the 1975 Helsinki statement, revised in 1983. A signed letter of informed consent was obtained from each patient. All patients received post-surgical radioiodine ablation and suppressive thyroxine therapy. Tumor recurrence was observed in three cases of FTC (Table 2). Tissue histology confirmed the initial diagnoses, as summarized in Table 2. Samples included ten FTA and thirteen FTC biopsies. In addition, eight patient-matched normal tissues obtained from patients with FTC (n=5) and FTA (n=3) were analyzed. Universal human reference total RNA (Stratagene, La Jolla, Calif., USA) was used as a control.

TABLE 2 Clinical and histologic data of patients tested by real time RT-PCR Age at Nodule Case diagnosis size Recur- PAX8-PPARG No. Diagnosis Sex (years) (mm) rence Rearrangement 1 FTA F 70 34 No Yes 2 FTA F 31 35 No Yes 3 FTA F 29 40 No Yes 4 FTA F 39 40 No NF¹¹ 5 FTA F 44 80 No NF 6 FTA F 51 40 No NF 7 FTA M 45 30 No NF 8 FTA F 52 30 No NF 9 FTA F 12 7 No NF 10 FTA F 22 15 No NF 11 FTC F 38 35 No Yes 12 FTC M 28 48 No Yes 13 FTC F 25 32 No Yes 14 FTC F 76 62 Yes Yes 15 FTC F 40 19 Yes NF 16 FTC F 38 32 No NF 17 FTC F 48 16 No NF 18 FTC F 36 20 No NF 19 FTC F 45 45 No NF 20 FTC F 24 23 No NF 21 FTC M 68 100 Yes NF 22 FTC F 33 30 No NF 23 FTC M 66 90 No NF ¹¹Not found in patient.

Cell Lines.

The human follicular thyroid carcinoma cell line UCLA RO-82W-1 (WRO), the papillary thyroid carcinoma line UCLA NPA-87-1 (NPA), and an undifferentiated thyroid carcinoma cell line UCLA RO-81A-1 (ARO) were grown in DMEM (Invitrogen, Carlsbad, Calif., USA) supplemented with 10% FCS (Invitrogen) in a 5% CO₂ environment at 37° C., as previously reported (18).

RNA Isolation, cDNA Synthesis, and Quantitative RT-PCR.

Total RNA was isolated using RNAgents (Promega, Madison, Wis., USA), according to the manufacture's recommendation. One microgram of total RNA was treated with DNA-free (AMBION, Austin, Tex., USA) and was reverse-transcribed to cDNA using the Omniscript Reverse Transcriptase kit (QIAGEN, Germantown, Md., USA) with oligo(dT)₁₂₋₁₈ primer and ten units of RNase inhibitor (Invitrogen). Reverse transcriptase-negative samples were prepared for each individual reaction and were used as controls for detection of assay contamination. The cDNA was then diluted 5-fold, and 1.5 μl aliquots were used in 20-μl PCR reactions containing 10-μM of each specific primer, 1× IQ Supermix (BioRad, Hercules, Calif., USA), and SYBR-Green (Sigma, St. Louis, Mo., USA). The PCR reaction was performed for 40 cycles of a 4-step program: 94° C. for 30 seconds, annealing temperature for 15 seconds, 72° C. for 15 seconds, and a fluorescence-read step for 10 seconds. After PCR, a melting curve analysis was performed and the read temperature of each assay was set above the melting point of short primer-dimers and below that of the target PCR product. Quantitative PCR reactions were performed twice in triplicate. The threshold cycles (Ct) were obtained using iCycler software version 3.0 (BioRad) and were averaged (SD≦1). Gene expression was normalized using the average of two control genes (ribosomal protein S8 and t-complex 1), shown by SAGE to be at equivalent levels in all three SAGE libraries. A relative expression amount was calculated according to the formula 2^((Rt-Et))/2^((Rn-En)). Rt is the Ct cycle number observed in the experimental sample for the two control genes. Et is the Ct cycle number observed in the experimental sample for the reference gene. Rn is the average Ct cycle number observed in ten adenomas for the two control genes. En is the average Ct cycle number observed in ten adenomas for the reference gene (22). FIG. 1 shows the relative expression levels of DDIT3, ARG2, ITM1, C1orf24, ACO1, and TARSH in normal tissue samples, ten FTA biopsy samples and thirteen FTC biopsy samples. The results obtained from fourteen of the seventeen relative expression levels in twenty-three samples and normal tissues were used for statistical analysis. Fourteen genes were used because three genes showed no difference by PCR. The PCR-specific primers, annealing temperatures, and fluorescence-read temperatures are summarized in Table 3. The PCR products were resolved by electrophoresis in a 3% agarose/ethidium gel.

TABLE 3 Primers and PCR conditions of selected genes and controls up regulated and down regulated in FTC Annealing Read Size Gene Primer¹² temp. temp.¹³ (bp)¹⁴ Controls RS8 F: 5′ AACAAGAAATACCGTGCCC 3′ 55 83 125 (SEQ ID NO: 41) R: 5′ GTACGAACCAGCTCGTTATTAG 3′ (SEQ ID NO: 42) TCP1 F: 5′ CACTAGCAGTTAATGCTGCC 3′ 57 81 123 (SEQ ID NO: 43) R: 5′ TGCTCAAATCAAGACCAATCC 3′ (SEQ ID NO: 44) Up regulated DDIT3 F: 5′ GCGACAGAGCCAAAATCAGAG 3′ 55 84 316 (SEQ ID NO: 45) R: 5′ AGTCAGCCAAGCCAGAGAAG 3′ (SEQ ID NO: 46) ARG2 F: 5′ GAAGGCATGTATATTGCTGAGG 54 84 204 3′ (SEQ ID NO: 47) R: 5′ TGAACTGGGAGTAGGAAGTTG 3′ (SEQ ID NO: 48) C1orf24 F: 5′ GCTTGATGAAACTCTGAAAGTG 57 86 180 3′ (SEQ ID NO: 49) R: 5′ AGAACTCCTGGCAGAATGG 3′ (SEQ ID NO: 50) PCSK2 F: 5′ CATCCCAGCCCCAATTTTC 3′ 54 86 183 (SEQ ID NO: 51) R: 5′ AATACTCCTGTCGCCTCTC 3′ (SEQ ID NO: 52) ODZ1 F: 5′ CGGCTTCAGACAAAAACTCAAG 57 83 180 3′ (SEQ ID NO: 53) R: 5′ AGAAGGGACAGCAGCAAAC 3′ (SEQ ID NO: 54) ACO1 F: 5′ TTTGAGAAAGAGCCATTGGGAG 54 83 300 3′ (SEQ ID NO: 55) R: 5′ TAGCAGCACATAGGCATCCAC 3′ (SEQ ID NO: 56) DNASE2 F: 5′ TTCCCTTCGCTCAGTTCTC 3′ 54 87 301 (SEQ ID NO: 57) R: 5′ ATGCCTACAGTTTTGTGCC 3′ (SEQ ID NO: 58) FLJ13576 F: 5′ ATTTCAGAGCAGTTGGTGTT 3′ 51.8 82.5 153 (SEQ ID NO: 59) R: 5′ GTTACCCAATTCATGGAAGA 3′ (SEQ ID NO: 60) ITM1 F: 5′ AGGCCTCACTGGGTATTCT 3′ 56 85 324 (SEQ ID NO: 61) R: 5′ TATCCTGACCAGCCAATGTTC 3′ (SEQ ID NO: 62) PDK4 F: 5′ CGCCTGTGATGGATAATTCC 3′ 54 81 120 (SEQ ID NO: 63) R: 5′ AGCATCTGTTCCATATCCTGA 3′ (SEQ ID NO: 64) DAP1 F: 5′ GAAAACAAGTGCCATTGCAAA 3′ 53 83 243 (SEQ ID NO: 65) R: 5′ GCTAAGCTGTCAGATATTT 3′ (SEQ ID NO: 66) PPP1R14B¹⁵ F: 5′ CAGCAGGCCAGAAATGAAG 3′ 54 87 226 (SEQ ID NO: 67) R: 5′ CGTCAAGTATGACCGCAAG 3′ (SEQ ID NO: 68) Down regulated COL14A1 F: 5′ CTGCCATCCTCAACCAGATT 3′ 55 88 211 (SEQ ID NO: 69) R: 5′ AACGCCTGGATTTCCTTTTT 3′ (SEQ ID NO: 70) TARSH F: 5′ TACTAGGCCCAAACCCAGTG 3′ 54 81 213 (SEQ ID NO: 71) R: 5′ CCTGGCTTTCCAGTGACATT 3′ (SEQ ID NO: 72) Emu1 F: 5′ TAAGGGAGACCCTGGTGAGAAG 3′ 54 83 131 (SEQ ID NO: 73) R: 5′ ACCCCAGCTCTGGTTCATAG 3′ (SEQ ID NO: 74) NID2 F: 5′ GTGCCGGAGTGGTTATGAGT 3′ 54 86 233 (SEQ ID NO: 75) R: 5′ TAGCTGCAGGGTGACATCTG 3′ (SEQ ID NO: 76) EDNRB¹⁶ F: 5′ TCCCGTTCAGAAGACAGCTT 3′ 57 83 231 (SEQ ID NO: 77) R: 5′ CACGAGGGCAAAGACAAGGAC 3′ (SEQ ID NO: 78) ¹²Specific primers that corresponded to exon-intron boundaries and were designed using Seq Web version 2. ¹³Fluorescence-read temperature. ¹⁴PCR product size. ¹⁵NOt confirmed by RT-PCR. ¹⁶Not confirmed by RT-PCR.

The results obtained from SAGE were compared with those obtained from RT-PCR analysis for the samples used to generate FTA and FTC libraries (cases 5 and 12, respectively). When RT-PCR and the original samples were used, fourteen of seventeen genes showed the predicted difference between FTA and FTC, and three did not.

Using the full panel of samples, nine of twelve FTC samples over-expressed transcripts maintained high expression in 50%-100% of FTCs tested, compared with the expression of same transcript in FTA and patient-matched normal tissue. DDIT3 (DNA-damage-inducible transcript 3) and ARG2 (arginase type II) were expressed at higher levels in FTCs. The increased average of expression was ≧5-fold in nearly all FTCs and some exhibited at least 11-fold higher levels as predicted by SAGE. The gene ITM1 (integral membrane protein 1) was expressed in all FTCs, with low levels of expression in six FTAs. The genes C1orf24 (niban) and ACO1 (aconitase 1) were expressed in 76% of FTCs, with low but detectable expression in 40% of FTAs. The hypothetical protein FLJ13576 was expressed in 67% of FTCs and in two cases of FTAs (cases 6 and 8). Six genes did not distinguish well: ODZ1, PCSK2 (proprotein convertase subtilisin/kexin type 2), DNASE2 (deoxyribonuclease II, lysosomal), LOC92196 (EST weakly similar to death-associated protein-1), PDK4 (pyruvate dehydrogenase kinase-4), and PPP1R14B (protein phosphatase-1, regulatory subunit-14B) were expressed in 30%-69% FTCs and in about 30%-40% of FTAs.

Of the fine genes predicted to be FTA specific, the TARSH gene was the only gene expressed at high levels in normal thyroid tissue and FTA tissue and not expressed in FTC tissue. Therefore, TARSH is a marker for diagnosing FTA. The genes putative Emu1, NID2, COL14A1, and endothelial receptor type B were expressed in about 60%-80% of FTCs and were not discriminatory between FTA and FTC. The RT-PCR results from the six genes that appeared to discriminate between FTC and FTA are summarized in FIG. 1. Although DDIT3 and ITM1 transcripts were elevated in most FTC cases, use of DDIT3 independently, for example, to identify tumors, could misclassify the case 14, which have low levels of DDIT3 but express ITM1 and ARG2 at higher levels (FIG. 1).

In addition, the expression levels of selected genes were analyzed in three well-characterized thyroid cell lines from different types of thyroid tumors (18, 27, 28). All the transcripts elevated in FTCs (see Table 1) were expressed in all thyroid cell lines. The expression of the candidate markers in the pure populations of cultured carcinoma cells indicates that the expression is due to the malignant component of the tumor. Conversely, the genes down regulated in the FTC library were present at lower levels or absent in the cell lines.

PPARG-PAX8 Rearrangement

All patient tissue samples tested by RT-PCR were concomitantly tested for the presence of PPARG-PAX8. Analysis revealed that the rearrangement between PPARG-PAX8, previously identified as a FTC marker (9), was found in about 33% of FTAs, and in 33% of FTC (Nakabashi et al., manuscript in preparation) and did not distinguish between adenoma and carcinoma (Table 2). The clinical and pathologic information was compared with the results obtained from quantitative RT-PCR analysis. Using the cross validation procedure, the prediction accuracy was estimated to be 83%. Four of the cases were misclassified (cases 6, 8, 13 and 21—Table 2). Case 6, which exhibited DDIT3, ARG2, and ITM1 expression, was re-evaluated by an experienced pathologist and showed no evidence of either capsule or blood vessel invasion. However, Hashimoto's thyroiditis and positive staining for both ERBB2 and P53 was reported. In case 8, Hashimoto's thyroiditis was also related. A longer follow-up for both cases will reveal whether they are true FTAs. Case 13 is a FTC that was diagnosed as minimally invasive. Case 21, however, is a FTC where both blood and capsule invasion were present.

Example 3 Analysis of Gene Expression by Immunohistochemical Staining

The expression levels of two genes (DDIT3 and ARG2) were confirmed by immunohistochemistry. For the immunohistochemical study pathologic materials were retrieved from specimens diagnosed with FTC (n=27) and FTA (n=32) at Hospital São Paulo, Federal University of São Paulo in an eight-year period from 1996-2003. Hematoxylin and eosin-stained sections were reviewed by an experienced pathologist.

Immunohistochemical Analysis.

Immunohistochemical staining was performed on paraffin-embedded tissue sections (3 μm) placed on 0.1% poly-lysine-coated slides (Sigma), deparaffinized with xylene and rehydrated through a series of graded alcohols. The endogenous alkaline phosphatase activity was blocked by 3% hydrogen peroxide. After pressure-cooking retrieval (10 mmol/L citrate buffer, pH 7.4 for 2 minutes), the sections were blocked in 1×PBS/0.1% BSA for 1 hour at room temperature and incubated with the first antibody for at least 16 hours at 4° C. The labelled streptavidin biotin reagents complex was used (DAKO LSAB+ kit, HRP; Dako Corp, Carpinteria, Calif., USA) with DAB as a substrate (Sigma). Hematoxylin was used as the nuclear counterstain. The slides were mounted in DAKO Faramount mounting medium (Dako Corp) and were examined by light microscopy. The immunopositivity was evaluated by two independent observers in a semiquantitative fashion in which the relative abundance of each antigen was evaluated by counting 1000 cells in at least five randomly chosen fields of the tissue sections at ×400 magnification and scored as follows: negative (−), weak (+), moderately abundant (++), and strong (+++). For two of the genes, DDIT3 and ARG2, antibodies were commercially available. Polyclonal antiserum GADD153, originated against a peptide mapping at the C-terminus of DDIT3 of human origin, was used at 1:200 dilution (R-20; Santa Cruz Biotechnologies Inc., Santa Cruz, Calif., USA). Polyclonal antiserum arginase II, raised against a recombinant protein to amino acids 291-354 mapping at the C-terminus of arginase II of human origin, was used at 1:100 dilution (H-64; Santa Cruz Biotechnologies Inc). Monoclonal mouse anti-human von Willebrand factor VIII was used at 1:25 dilution (M0616; Dako Corp). CA9 mouse monoclonal G250 antibody (gift of E. Oosterwijk, University Medical Center, Nijmegen, The Netherlands) was used at 1:400 dilution. The control for antibody specificity included incubation with rat IgG, used in the same concentration as the first antibody (Vector Laboratories). Positive and negative controls were included in each run.

The results are summarized in table 4 (to details see Table 5). Staining for DDIT3 expression (GADD153 antibody) showed a moderate to strong (++/+++) expression in twenty-three FTCs (85.2%). The staining was detected in both the nucleus and the cytoplasm of neoplastic follicular cells (FIGS. 3D, 3E and 3F). Adjacent nonneoplastic thyroid tissue did not stain. Three of four FTCs negative for DDIT3 staining were FTC minimally invasive (focal capsular and vascular invasion) and one was moderately differentiated. No nuclear and cytoplasmic staining in epithelial cells was observed in twenty nine (90.6%) sections from FTAs (FIGS. 3A, 3B and 3C) and IgG-negative controls. A weak or moderate staining for DDIT3 was found in 3 (9.4%) of FTAs. Two of three were diagnosed as hürthle cell adenoma (HCA) and one was an atypical adenoma (data not shown). Immunohistochemistry analysis revealed the expression of DDIT3 in three FTAs, which were diagnosed as atypical adenoma and hürthle cell adenoma (Table 5). This results support the idea that some follicular hürthle tumors should be considered a separate thyroid cancer class and few FTAs are early in situ carcinomas with malignant potential. Longer follow-up will be needed to determine whether these tumors are a less benign variant. In addition, both follicular lesions coexisted with Hashimoto's thyroiditis, which is a possible source of diagnostic error (9). Immunohisotchemistry analysis in a large set of hürthle adenomas would be necessary to better understand whether the use of additional class predicted gene in combination with DDIT3 and ARG2 can better classify these type of follicular lesions or if additional profiling is necessary to find new markers for the hürthle subtype.

TABLE 4 Immunoreactivity for DDIT3 and ARG2 in FTA and FTC. Follicular Thyroid Follicular Thyroid Immunoexpression¹⁷ Adenoma (n = 32) Carcinoma (n = 27) DDIT3 − 29 (90.6%)  4 (14.8%) + 2 (6.3%) 0 ++ 1 (3.1%)  5 (18.5%) +++ 0 18 (66.7%) ARG2 − 29 (90.6%)  4 (14.8%) + 1 (3.1%) 0 ++ 2 (6.3%) 2 (7.4%) +++ 0 21 (77.8%) DDIT3/ARG2 (−/−) 29 3 DDIT3/ARG2 (−/+) 0 1 DDIT3/ARG2 (+/−) 0 1 DDIT3/ARG2 (+/+) 3 23

TABLE 5 Clinical and Pathological features of FTA and FTC analyzed by immunohistochemistry to DDIT3 and ARG2. Case No. FNA¹⁸ SEX¹⁹ AGE DDIT3²⁰ ARG2²⁰ FTA (n = 32)  1 SUS F 23 (−) (−)  2 BNG F 54 (−) (−)  3 SUS F 30 (−) (−)  4 SUS F 42 (−) (−)  5 SUS F 39 (−) (−)  6 SUS F 39 (−) (−)  7 SUS F 27 (−) (−)  8 SUS F 39 (−) (−)  9²¹ SUS F 16 (++) (+) 10 BNG F 47 (−) (−) 11 SUS F 51 (−) (−) 12 SUS F 17 (−) (−) 13 SUS M 42 (−) (−) 14 NA F 54 (−) (−) 15 SUS M 74 (−) (−) 16 NA F 22 (−) (−) 17 NA M 51 (−) (−) 18 SUS M 53 (−) (−) 19 SUS F 38 (−) (−) 20 NA M 55 (−) (−) 21 SUS F 37 (−) (−) 22 SUS F 38 (−) (−) 23 SUS F 49 (−) (−) 24 SUS F 62 (−) (−) 25 NA F 34 (−) (−) 26 BNG F 43 (−) (−) 27 NA F 29 (−) (−) 28 NA F 72 (−) (−) 29 NA F NA (−) (−) 30 NA M 38 (−) (−) 31²² SUS F 34 (+) (++) 32²² SUS F 29 (+) (++) FTC (n = 27) 33 SUS F 68 (+++) (+++) 34 SUS F 17 (++) (+++) 35 SUS F 49 (+++) (+++) 36 SUS F 61 (+++) (+++) 37²³ SUS F 33 (−) (−) 38 SUS F 61 (+++) (+++) 39 NA F 21 (++) (++) 40²⁴ M 75 (−) (++) 41²³ F 23 (−) (−) 42 M 62 (+++) (+++) 43²³ CA M 66 (−) (−) 44 CA M 75 (+++) (++) 45 NA F 60 (+++) (+++) 46 NA F 52 (++) (++) 47 NA F 76 (++) (++) 48 F 47 (+++) (+++) 49 NA F 75 (+++) (+++) 50 M 36 (+++) (+++) 51 NA F NA (+++) (+++) 52²³ F 24 (++) (−) 53 NA F 59 (+++) (+++) 54 F 69 (+++) (++) 55 NA F 38 (++) (+++) 56 NA M 31 (+++) (+++) 57 F 66 (++) (+++) 58 F 59 (+++) (++) 59 F 34 (+++) (+++) ¹⁷Negative (−), Positive (+). The intensity was scored into three categories: weak (+), moderate (++), strong (+++) ¹⁸Results obtained from FNA biopsy. (SUS) suspicious, non-available (NA), cancer (CA) and benign (BNG). ¹⁹Female (F) and Male (M). ²⁰Negative (−), Positive (+). The intensity was scored into three categories: weak (+), moderate (++), strong (+++) ²¹Atypical adenoma. ²²Hürthle cell adenoma. ²³Minimally invasive. ²⁴Moderately differentiated

In this study, over expression of DDIT3 transcript was found in FTCs and thyroid cancer cell lines Immunohistochemistry showed DDIT3 protein expression was moderate to strong in twenty three (82.5%) of FTCs, and specific for the follicular cells of the tumor (FIGS. 3A-3F). No expression of DDIT3 was found in four FTCs, three of which were diagnosed as minimally invasive. This observation suggested a correlation with DDIT3 expression and capsular and vascular invasion. Barden et al. (16), by oligonucleotide array, found the gene DDIT3 up regulated in FTC and the genes putative Emu1 and NID2 up regulated in FTA. The investigators did not validate the expression of these genes in a set of samples. Interestingly, Nikiforova et al. (35) reported that 85% of FTC could develop through non-overlapping RAS or PAX8-PPARG pathways. The authors suggested that RAS activation by itself appears insufficient to determine malignant growth but may predispose to acquisition of additional genetic or epigenetic alteration that lead to a fully transformed phenotype. Brenner et al. showed a signaling cascade from FAS receptor via the G proteins RAS and RAC to JNK/p38-K and the transcription factor DDIT3 (36). Expression of DDIT3 was also elevated after induction with thiazolidinedione via PPARG1 (37). It is therefore possible that either or both of these pathways activate DDIT3 expression.

ARG2 staining was consistently negative in twenty nine of FTAs (90.6%,) and adjacent nonneoplastic thyroid tissue, whereas specific staining was found in the cytoplasm of neoplastic follicular thyroid cells in twenty three of FTCs (85.2%) analyzed (FIGS. 3G, 3H, 3I, 3J, 3K and 3L). All four FTCs, negative for ARG2 were diagnosed as minimally invasive.

Overall, a moderate/strong expression of ARG2 and DDIT3 were observed in 85.2% of FTCs, whereas 90.6% of FTAs were negative, indicating the utility of these antibody to discriminate FTC from FTA. In addition, the immunoreactivity with both antibodies in FTCs were more often diffuse than focal and stronger intensity in comparison with those observed in the four cases of FTAs.

Staining with von Willebrand factor VIII was used to distinguish endothelial cells in all tissues. Moderate expression of CA9 was observed in 2 FTCs (cases 11 and 12), but not in FTAs and normal tissues (data not shown).

Example 4 Statistical Analysis for a Class Predictor to Differentiate FTA from FTC

To identify genes for which expression levels were statistically significant between FTA and FTC, the relative expression data obtained from RT-PCR analysis on fourteen of seventeen genes (FIG. 1) were used. The initial comparison of expression levels was carried out using rank based (Wilcoxon rank sum) and mean based (Student's t) tests. Data were log transformed before applying the Student's t test. A comparison was designated as statistically significant if either the rank-sum statistic or the corresponding t-statistic was found to be significant, using an alpha level that had been adjusted (using a Bonferroni adjustment) to keep the family wise error rate at 0.10. Next, development of an expression-based model that could be used to predict class of diagnosis for the tumor (FTA or FTC) was investigated. The framework outlined by Radmacher et al. (23) was followed, and the prediction method we used was the compound covariate predictor for gene expression data (23, 24). The performance of the predictor was tested using leave-one-out cross-validation for all steps of the prediction procedure (i.e., selection of differentially expressed genes as well as creation of the prediction rule) (23, 25). The significance of the performance of the predictor using the permutation based test outlined in Radmacher et al. (23) was assessed, in which the class labels are randomly permuted and the proportion of data sets that have a cross validated error rate as small as observed in the data set was calculated. Because it was prohibitive to compute all possible permutations, 2000 random permutations were used to estimate the achieved significance level. The concordance of the results of the immunohistochemistry staining and the pathological identification of class (FTC vs. FTA) was estimated using a kappa statistic and constructing a 95% confidence interval (Kramer and Feinstein 1981). The use of kappa corrects for agreements between the two methods (immunohistochemistry and pathology) expected by chance. The maximum value of kappa, corresponding to perfect agreement, is 1.0. Kramer and Feinstein (1981) suggested guidelines to assess the significance of the magnitude of the statistic.

Genes were declared different between the 2 groups if the P value was less than the family-wise error rate of 0.10. The Wilcoxon test showed that the difference in gene expression of DDIT3, ARG2, and ITM1 was statistically significantly at the 0.05 level. Expression of an additional gene (C1orf24) was statistically significant at the 0.10 level. The Student's t test showed that genes DDIT3 and ITM1 were significant at the 0.05 level. No additional genes were significant at the 0.10 level. Thus, expression levels of four genes (DDIT3, ARG2, ITM1, and C1orf24) were declared significantly different between the two groups; expression levels of DDIT3 and ITM1 were declared significantly different by both analyses.

The class predictor used genes in which expression levels were found significantly different at the 0.10 level using the t test. The sample t statistics were used as weights in the compound covariate predictor. To evaluate the predictor, the leave-one-out cross-validation was used: for each sample, in turn, one sample was left out, and the predictor was developed on the remaining twenty two samples. The left-out sample was predicted. All the steps of the prediction procedure were used including selection of differentially expressed genes, as well as creation of the prediction rule (23, 25). Using leave-one-out cross validation, nineteen of the twenty three (83%) samples were correctly predicted. To assess the significance of these prediction results, a permutation test (23, 25) was implemented. The proportion of random permutations with four or fewer misclassifications was 0.007. Thus, the results of the prediction analysis were found significant. Two of the genes, DDIT3 and ITM1, were always selected in each step of the cross validation procedure (i.e., each time a sample was left out). In the Wilcoxon test, ARG2 was statistically significantly at the 0.05 level. An additional gene, C1orf24, expressed in most of the FTCs, can be a predictor. Even when starting with only one SAGE library per tumor to predict candidate markers, and faced with heterogeneous gene expression in FTA and FTC, we were still able to find consistent and statistically significant markers. However, future studies using this simple SAGE-based method to identify tumor markers would likely benefit from using two or more libraries of each representative tumor type. SAGE has been previously used for a shallower transcript sampling of thyroid tissue, but not specifically directed toward distinguishing between FTA and FTC (29-31). Using deep sampling of representative FTC and FTA cases allowed application of selection criteria for candidate genes that were likely to have large differences in expression that could be easily detected by immunohistochemistry.

FIG. 2 shows the final products for three genes, the differential expression of which was shown to be statistically significant at the 0.05 level, after running forty cycles of PCR using templates from FTC, FTA, normal thyroid, and cell lines.

The concordance between the results of the immunohistochemistry staining on an independent set of tumors and the diagnosis by histopathology was estimated by kappa. The estimated kappa was 0.76 with a 95% confidence interval of [0.59, 0.93]. The value of 0.76 corresponds to a substantial strength of agreement based on previously developed guidelines (Kramer and Feinstein 1981).

Example 5 TARSH and its Binding Partner NESH can Repress Cell Invasion Phenotype In Vitro

To investigate whether TARSH and its partner NESH could repress cell invasion in vitro, TARSH and NESH full-length cDNAs were re-expressed in two thyroid carcinoma cell lines ARO and WRO. These cell lines have an invasive phenotype (27, 28).

In Vitro Invasion Assay.

A neomycin-selectable expression vector pcDNA3.1 (Invitrogen) containing a full-length wildtype (wt) human TARSH was stably transfected into ARO and WRO thyroid carcinoma cell lines. Additionally, a full-length cDNA of NESH, TARSH partner, was transfected into ARO and WRO cell lines (gift of S. Matsuda, Nagoya University School of Medicine, Nagoya, Japan). Cells (5×10⁶) were transfected using Bio-Rad Gene Pulser according to the manufacturer's instruction (Bio-Rad). Neomycin resistant colonies were initially selected on plates containing 800 μg/mL geneticin (G418; Gibco-BRL, Gaithersburg, Md., USA) and maintained in culture medium containing 600 μg/mL geneticin. As a control, cells were transfected with pcDNA3.1 and selected as described above. Two stable transfected clones for TARSH, NESH and a control were selected and used for an invasion assay using a BD Biocoat Matrigel Invasion Chamber as described by the manufacture (Becton Dickson Labware, Bedford, Mass., USA). Briefly, the invasion chamber (with Matrixgel matrix) and control chamber (without Matrixgel matrix) were rehydrated and 2.5×10⁴ cells were plated to the chambers containing Matrigel matrix and control chamber wells without matrix. Twenty-four hours later, the cells on the lower side of the chambers were fixed and stained with Baxter Diff-Quik stain kit (Dade Behring, Newark, Del., USA) and random fields were counted under the light microscopy with a standardized grid. The plates were done in triplicates and the invasion index was expressed by the percent of cells that invade through occluded membrane (Invasion Chamber) divided by the percent of cells that migrated trough the uncoated membrane (control insert).

The invasion index observed was 7.5 and 5.1 respectively for control chambers compared to invasion chambers. An elevated migratory response was observed in control chamber, compared to the invasion chamber. Additionally, the number of invading cells from clones with vector only were compared to the number of invading cells from clones that re-expressed TARSH or NESH. The results obtained were similar to those obtained with control vs. invasion chamber. Even though these are preliminary results, it suggests that TARSH and NESH re-expression in thyroid carcinoma cell lines suppress cell motility and invasion. It is perhaps not too surprising that expression of some of the genes differing between adenoma and carcinoma might be involved in tumor invasion, a main distinguishing feature between benign and malignant tumors.

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1. A method for distinguishing follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC) comprising the steps of: determining amount of an expression product of at least one gene selected from the group consisting of DDIT3, ARG2, ITM1, C1orf24, TARSH, and ACO1 in a test follicular thyroid specimen; comparing the amount in the test specimen to the amount in a normal control thyroid specimen, wherein the expression product is selected from the group consisting of protein and RNA; identifying the test follicular thyroid specimen as FTA if the amount of expression product of TARSH is equal to or greater in the test follicular thyroid specimen than in the normal control thyroid specimen, or identifying the test follicular thyroid specimen as FTC if the amount of expression product of DDIT3, ARG2, ITM1, C1orf24, or ACO1 is greater in the test follicular thyroid specimen than in the normal control thyroid specimen.
 2. The method of claim 1 wherein the at least one gene comprises DDIT3.
 3. The method of claim 1 wherein the at least one gene comprises ARG2.
 4. The method of claim 1 wherein the at least one gene comprises ITM1.
 5. The method of claim 1 wherein the at least one gene comprises C1orf24.
 6. The method of claim 1 wherein the at least one gene comprises TARSH.
 7. The method of claim 1 wherein the at least one gene comprises ACO1.
 8. The method of claim 1 wherein the test follicular thyroid specimen is a fine-needle aspiration biopsy.
 9. The method of claim 1 wherein the test follicular thyroid specimen is a pre-operative specimen.
 10. The method of claim 1 wherein the amount of RNA is compared.
 11. The method of claim 10 wherein the amount of RNA is determined by reverse transcriptase-polymerase chain reaction (RT-PCR).
 12. The method of claim 1 wherein the amount of protein is compared.
 13. The method of claim 12 wherein the amount of protein is determined using an antibody.
 14. The method of claim 13 wherein the antibody is contacted with a histological preparation of the test follicular thyroid specimen.
 15. The method of claim 1 wherein the follicular thyroid specimen is identified as FTC if the amount of at least two of said expression products of DDIT3, ARG2, ITM1, C1orf24, and ACO1 is greater in the test follicular thyroid specimen than in the normal control thyroid specimen.
 16. The method of claim 1 wherein the follicular thyroid specimen is identified as FTC if the amount of at least three of said expression products of DDIT3, ARG2, ITM1, C1orf24, and ACO1 is greater in the test follicular thyroid specimen than in the normal control thyroid specimen.
 17. The method of claim 1 wherein the follicular thyroid specimen is identified as FTC if the amount of at least four of said expression products of DDIT3, ARG2, ITM1, C1orf24, and ACO1 is greater in the test follicular thyroid specimen than in the normal control thyroid specimen.
 18. The method of claim 1 wherein the follicular thyroid specimen is identified as FTC if the amount of at least five of said expression products of DDIT3, ARG2, ITM1, C1orf24, and ACO1 is greater in the test follicular thyroid specimen than in the normal control thyroid specimen.
 19. The method of claim 1 wherein the step of determining employs a technique selected from the group consisting of: microarray analysis, northern blot analysis, differential hybridization, ribonuclease protection, enzyme-linked immunosorbent assay (ELISA), immunoprecipitation, western blot, radioimmunoassay, and fluorescence activated cell sorting.
 20. The method of claim 1 further comprising the step of aspirating using a fine needle to form the test follicular thyroid specimen.
 21. The method of claim 1 further comprising the step of obtaining a test follicular thyroid specimen prior to surgery.
 22. A method for distinguishing follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC) comprising the steps of: determining amount of an expression product of DDIT3, ARG2, and ITM1 in a test follicular thyroid specimen; comparing the amount in the test sample to the amount in a normal control thyroid specimen wherein the expression product is selected from the group consisting of protein and RNA; identifying the test follicular thyroid specimen as FTC if the amount of expression product of DDIT3, ARG2, or ITM1 is increased in the test follicular thyroid specimen relative to the normal control thyroid specimen.
 23. The method of claim 22 wherein the test follicular thyroid specimen is a fine-needle aspiration biopsy.
 24. The method of claim 22 wherein the test follicular thyroid specimen is a pre-operative specimen.
 25. The method of claim 22 wherein the amount of RNA is compared.
 26. The method of claim 25 wherein the amount of RNA is determined by reverse transcriptase-polymerase chain reaction (RT-PCR).
 27. The method of claim 22 wherein the amount of protein is compared.
 28. The method of claim 27 wherein the amount of protein is determined using an antibody.
 29. The method of claim 28 wherein the antibody is contacted with a histological preparation of the test follicular thyroid specimen.
 30. The method of claim 22 wherein the follicular thyroid specimen is identified as FTC if DDIT3 and ARG2 are increased.
 31. The method of claim 22 wherein the follicular thyroid specimen is identified as FTC if DDIT3 and ITM1 are increased.
 32. The method of claim 22 wherein the follicular thyroid specimen is identified as FTC if ITM1 and ARG2 are increased.
 33. The method of claim 22 wherein the follicular thyroid specimen is identified as FTC if DDIT3, ARG2, and ITM1 are increased.
 34. The method of claim 22 wherein the step of determining employs a technique selected from the group consisting of: microarray analysis, northern blot analysis, differential hybridization, ribonuclease protection, enzyme-linked immunosorbent assay (ELISA), immunoprecipitation, western blot, radioimmunoassay, and fluorescence activated cell sorting.
 35. The method of claim 22 further comprising the step of aspirating using a fine needle to form the test follicular thyroid specimen.
 36. The method of claim 22 further comprising the step of obtaining a test follicular thyroid specimen prior to surgery. 